A multicomponent T<sub>2</sub> relaxometry algorithm for myelin water imaging of the brain

Björk, Marcus; Zachariah, Dave; Kullberg, Joel; Stoica, Petre

doi:10.1002/mrm.25583

Cited by 27 publications

(21 citation statements)

References 41 publications

(73 reference statements)

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“…Detection and characterization of multiple components of relaxation in the brain are useful. A number of studies have demonstrated that several applications utilized multicomponent T 2 in assessing white matter pathology and myelin . The short‐ and long‐T 2 components in the biexponential T 2 relaxation model represent anatomical compartmentation of tissue water, probably intra‐ and extracellular water .…”

Section: Discussionmentioning

confidence: 99%

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Zhu

Liu

Ying

et al. 2019

Magnetic Resonance in Med

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Purpose To develop and evaluate a fast imaging method based on signal‐compensated low‐rank plus sparse matrix decomposition to accelerate data acquisition for biexponential brain T1ρ mapping (Bio‐SCOPE). Methods Two novel strategies were proposed to improve reconstruction performance. A variable‐rate undersampling scheme was used with a varied acceleration factor for each k‐space along the spin‐lock time direction, and a modified nonlinear thresholding scheme combined with a feature descriptor was used for Bio‐SCOPE reconstruction. In vivo brain T1ρ mappings were acquired from 4 volunteers. The fully sampled k‐space data acquired from 3 volunteers were retrospectively undersampled by net acceleration rates (R) of 4.6 and 6.1. Reference values were obtained from the fully sampled data. The agreement between the accelerated T1ρ measurements and reference values was assessed with Bland‐Altman analyses. Prospectively undersampled data with R = 4.6 and R = 6.1 were acquired from 1 volunteer. Results T1ρ‐weighted images were successfully reconstructed using Bio‐SCOPE for R = 4.6 and 6.1 with signal‐to‐noise ratio variations <1 dB and normalized root mean square errors <4%. Accelerated and reference T1ρ measurements were in good agreement for R = 4.6 (T1ρs: 18.6651 ± 1.7786 ms; T1ρl: 88.9603 ± 1.7331 ms) and R = 6.1 (T1ρs: 17.8403 ± 3.3302 ms; T1ρl: 88.0275 ± 4.9606 ms) in the Bland‐Altman analyses. T1ρ parameter maps from prospectively undersampled data also show reasonable image quality using the Bio‐SCOPE method. Conclusion Bio‐SCOPE achieves a high net acceleration rate for biexponential T1ρ mapping and improves reconstruction quality by using a variable‐rate undersampling data acquisition scheme and a modified soft‐thresholding algorithm in image reconstruction.

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Section: Discussionmentioning

confidence: 99%

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Zhu

Liu

Ying

et al. 2019

Magnetic Resonance in Med

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“…Parametric time-domain methods, due to their superior resolution, have been shown to surpass their nonparametric counterparts. [8] Several parametric estimators for multiple-damped exponentials-such as filter diagonalization method, [9] Hankel singular value decomposition, [10] Hankel total least squares, [6] complete reduction to amplitude frequency table (CRAFT), [11] and matrix pencil [12] have been shown to accurately quantitate and extract the parameters of the FID signal. [13] However, many of these methods (though not CRAFT) require the number of cisoids in the signal model to be known a priori, and this can introduce a systematic bias into their estimates.…”

Section: Prior Workmentioning

confidence: 99%

“…[15] This method has been shown to exhibit superior performance over most of the existing methods when applied to the extraction of T 2 relaxation parameters for myelin water imaging of the brain-a problem that employs such an exponential signal model. [8] Also, it has been shown to be completely user-input free. These advantages make the SM method an excellent choice for the extraction of FID parameters.…”

Section: Prior Workmentioning

confidence: 99%

“…Parametric time‐domain methods, due to their superior resolution, have been shown to surpass their nonparametric counterparts . Several parametric estimators for multiple‐damped exponentials—such as filter diagonalization method, Hankel singular value decomposition, Hankel total least squares, complete reduction to amplitude frequency table (CRAFT), and matrix pencil—have been shown to accurately quantitate and extract the parameters of the FID signal .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the Steiglitz‐McBride method (SM) has been applied to extract the parameters of an exponential signal model . This method has been shown to exhibit superior performance over most of the existing methods when applied to the extraction of T 2 relaxation parameters for myelin water imaging of the brain—a problem that employs such an exponential signal model . Also, it has been shown to be completely user‐input free.…”

Section: Introductionmentioning

confidence: 99%

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A subband Steiglitz‐McBride algorithm for automatic analysis of FID data

Anjum

Dmochowski

Teal

2018

Magnetic Reson in Chemistry

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Fast, accurate, and automatic extraction of parameters of nuclear magnetic resonance free induction decay (FID) signal for chemical spectroscopy is a challenging problem. Recently, the Steiglitz-McBride algorithm has been shown to exhibit superior performance in terms of speed, accuracy, and automation when applied to the extraction of T relaxation parameters for myelin water imaging of brain. Applying it to FID data reveals that it falls short of the second objective, the accuracy. Especially, it struggles with the issue of missed spectral peaks when applied to chemical samples with relatively dense frequency spectra. To overcome this issue, a preprocessing stage of subband decomposition is proposed before the application of Steiglitz-McBride algorithm to the FID signal. It is demonstrated that by doing so, a considerable improvement in accuracy is achieved. But this is not gained at the cost of the first objective, the speed. An adaptive subband decomposition is employed in conjunction with the Bayesian information criteria to carry out an efficient decomposition according to spectral content of the signal under investigation. Furthermore, adaptive subband decomposition and the Bayesian information criteria also serve to make the resulting algorithm independent of user input, which also fulfills the third objective, the automation. This makes the proposed algorithm favorable for fast, accurate, and automatic extraction of FID signal parameters.

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Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

Feng,

Jia,

Peng

et al. 2023

Medical Physics

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Background: Magnetic nanoparticles (MNPs) are used as tracers without ionizing radiation in vascular imaging, molecular imaging, and neuroimaging. The relaxation mechanisms of magnetization in response to excitation magnetic fields are important features of MNPs. The basic relaxation mechanisms include internal rotation (Néel relaxation) and external physical rotation (Brownian relaxation). Accurate measurement of these relaxation times may provide high sensitivity for predicting MNP types and viscosity-based hydrodynamic states. It is challenging to separately measure the Néel and Brownian relaxation components using sinusoidal excitation in conventional MPI. Purpose: We developed a multi-exponential relaxation spectral analysis method to separately measure the Néel and Brownian relaxation times in the magnetization recovery process in pulsed vascular MPI. Methods: Synomag-D samples with different viscosities were excited using pulsed excitation in a trapezoidal-waveform relaxometer. The samples were excited at different field amplitudes ranging from 0.5 to 10 mT at intervals of 0.5 mT. The inverse Laplace transform-based spectral analysis of the relaxationinduced decay signal in the field-flat phase was performed by using PDCO, a primal-dual interior method for convex objectives. Néel and Brownian relaxation peaks were elucidated and measured on samples with various glycerol and gelatin concentrations. The sensitivity of viscosity prediction of the decoupled relaxation times was evaluated. A digital vascular phantom was designed to mimic a plaque with viscous MNPs and a catheter with immobilized MNPs. Spectral imaging of the digital vascular phantom was simulated by combining a field-free point with homogeneous pulsed excitation. The relationship between the Brownian relaxation time from different tissues and the number of periods for signal averages was evaluated for a scan time estimation in the simulation.Xin Feng and Guang Jia contributed equally to the work.

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A multicomponent T₂ relaxometry algorithm for myelin water imaging of the brain

Cited by 27 publications

References 41 publications

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

A subband Steiglitz‐McBride algorithm for automatic analysis of FID data

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

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A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain

Cited by 27 publications

References 41 publications

Bio‐SCOPE: fast biexponential T1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

A subband Steiglitz‐McBride algorithm for automatic analysis of FID data

Relaxation spectral analysis in multi‐contrast vascular magnetic particle imaging

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A multicomponent T₂ relaxometry algorithm for myelin water imaging of the brain

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition

Bio‐SCOPE: fast biexponential T_1ρ mapping of the brain using signal‐compensated low‐rank plus sparse matrix decomposition